mirror of
https://github.com/BerriAI/litellm.git
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c168199e33
* test(ci): repair stale request fakes, spend-log golden, auto-router labels, and Interactions spec lookups Request fakes now carry the scope a real Starlette request has, the GCS pub/sub spend-log golden gains the agent identity keys from #43722, the auto-router session tests follow the baseline_models contract from #43348, and the Interactions spec checks resolve the create body and resource paths from the live spec instead of hardcoded names * test(ci): move retired OpenAI text-completion fixtures to live vehicles OpenAI still serves native /v1/completions on the gpt-5.4 family, so the single-prompt cases move to text-completion-openai/gpt-5.4-nano. Multi-prompt batches and echo with logprobs now 500 on every OpenAI model, so those cases keep the same text-completion-openai transport pointed at Fireworks, which documents both. The optional-params test asserts the request body actually sent instead of a success callback whose assertions were swallowed * test(ci): use a serverless Fireworks model for the text-completion batch and echo cases gpt-oss-20b is on-demand only on Fireworks, so the CI key got 404 model not deployed; glm-5p3-flash is listed as serverless * test(ci): skip the ROI calculator repository listing in the security route sweep GET /roi-calculator/repositories (#43669) lists repositories from the configured GitHub API, api.github.com by default, so the S2 sweep's GET of every route made the owned proxy reach an external host and failed the egress check in 31 integration-security tests. It joins /get/latest_release_info in the deny list
4201 lines
85 KiB
Python
4201 lines
85 KiB
Python
import asyncio
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from typing import Final
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import json
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import os
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import traceback
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from types import MappingProxyType
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from dotenv import load_dotenv
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load_dotenv()
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import io
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from unittest.mock import MagicMock, patch
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import pytest
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import litellm
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from litellm import (
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RateLimitError,
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TextCompletionResponse,
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atext_completion,
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completion,
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completion_cost,
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embedding,
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text_completion,
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)
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litellm.num_retries = 3
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FIREWORKS_TEXT_COMPLETION: Final = MappingProxyType(
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{
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"model": "text-completion-openai/accounts/fireworks/models/glm-5p3-flash",
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"api_base": "https://api.fireworks.ai/inference/v1",
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"api_key": os.environ.get("FIREWORKS_AI_API_KEY"),
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}
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)
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token_prompt = [
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[
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32,
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2043,
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32,
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329,
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4585,
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262,
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1644,
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14,
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34,
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3705,
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319,
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616,
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47551,
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30,
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930,
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19219,
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284,
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284,
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428,
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355,
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1790,
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|
355,
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|
1744,
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13,
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40,
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|
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|
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13,
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407,
|
|
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|
475,
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|
356,
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389,
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|
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|
|
11,
|
|
673,
|
|
3848,
|
|
502,
|
|
38074,
|
|
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|
|
290,
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|
356,
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|
423,
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|
3993,
|
|
13801,
|
|
11,
|
|
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|
|
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|
|
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|
|
3503,
|
|
13,
|
|
220,
|
|
198,
|
|
198,
|
|
17,
|
|
812,
|
|
2084,
|
|
25737,
|
|
373,
|
|
287,
|
|
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|
|
422,
|
|
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|
|
13230,
|
|
13,
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|
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|
|
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|
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|
|
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|
|
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290,
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|
|
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|
|
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|
|
13,
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|
550,
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|
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|
|
5110,
|
|
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|
|
290,
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|
262,
|
|
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|
|
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|
|
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|
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290,
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|
|
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|
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|
|
13,
|
|
383,
|
|
5156,
|
|
338,
|
|
9955,
|
|
11,
|
|
25737,
|
|
338,
|
|
13850,
|
|
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|
|
468,
|
|
257,
|
|
47973,
|
|
14,
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|
9979,
|
|
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|
|
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|
290,
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|
373,
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503,
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286,
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|
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|
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|
362,
|
|
1933,
|
|
523,
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|
339,
|
|
2492,
|
|
470,
|
|
612,
|
|
329,
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|
477,
|
|
286,
|
|
428,
|
|
13,
|
|
220,
|
|
198,
|
|
198,
|
|
3347,
|
|
10667,
|
|
5223,
|
|
503,
|
|
706,
|
|
513,
|
|
1528,
|
|
11,
|
|
23630,
|
|
673,
|
|
373,
|
|
366,
|
|
38125,
|
|
290,
|
|
655,
|
|
2622,
|
|
257,
|
|
3338,
|
|
8399,
|
|
1911,
|
|
314,
|
|
2298,
|
|
607,
|
|
510,
|
|
11,
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|
1011,
|
|
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|
|
284,
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607,
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2156,
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290,
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|
673,
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|
3393,
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284,
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|
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|
|
20349,
|
|
290,
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|
4144,
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|
257,
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|
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|
|
13,
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314,
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|
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|
470,
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|
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|
|
318,
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|
257,
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290,
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819,
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5722,
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329,
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604,
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1933,
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2739,
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329,
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720,
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422,
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2156,
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11,
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290,
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373,
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12165,
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503,
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286,
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376,
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666,
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220,
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673,
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|
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393,
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|
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|
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|
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|
|
290,
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
13952,
|
|
1994,
|
|
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|
|
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|
|
287,
|
|
11,
|
|
766,
|
|
399,
|
|
8535,
|
|
2712,
|
|
351,
|
|
36062,
|
|
287,
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262,
|
|
5228,
|
|
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|
|
25737,
|
|
3804,
|
|
503,
|
|
319,
|
|
262,
|
|
18507,
|
|
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|
|
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|
|
16914,
|
|
319,
|
|
262,
|
|
6891,
|
|
3084,
|
|
13,
|
|
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|
|
2406,
|
|
422,
|
|
257,
|
|
1641,
|
|
47655,
|
|
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|
|
13230,
|
|
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|
|
314,
|
|
760,
|
|
644,
|
|
16914,
|
|
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|
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|
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|
|
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|
|
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|
|
881,
|
|
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|
|
373,
|
|
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|
|
475,
|
|
314,
|
|
714,
|
|
423,
|
|
23529,
|
|
276,
|
|
340,
|
|
510,
|
|
290,
|
|
5901,
|
|
616,
|
|
18057,
|
|
351,
|
|
340,
|
|
13,
|
|
314,
|
|
6810,
|
|
19772,
|
|
2024,
|
|
8347,
|
|
287,
|
|
262,
|
|
2166,
|
|
2119,
|
|
290,
|
|
399,
|
|
8535,
|
|
373,
|
|
287,
|
|
3294,
|
|
11685,
|
|
286,
|
|
8242,
|
|
290,
|
|
607,
|
|
7374,
|
|
15224,
|
|
13,
|
|
383,
|
|
4894,
|
|
373,
|
|
572,
|
|
13,
|
|
383,
|
|
2156,
|
|
373,
|
|
3863,
|
|
2319,
|
|
37,
|
|
532,
|
|
340,
|
|
373,
|
|
1542,
|
|
2354,
|
|
13,
|
|
220,
|
|
198,
|
|
198,
|
|
40,
|
|
1718,
|
|
399,
|
|
8535,
|
|
284,
|
|
616,
|
|
1097,
|
|
11,
|
|
290,
|
|
1444,
|
|
16679,
|
|
329,
|
|
281,
|
|
22536,
|
|
355,
|
|
314,
|
|
373,
|
|
12008,
|
|
25737,
|
|
373,
|
|
14904,
|
|
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262,
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|
7723,
|
|
1637,
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284,
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3758,
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607,
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14321,
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290,
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477,
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286,
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11,
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|
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6135,
|
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|
|
|
|
|
|
def test_unit_test_text_completion_object():
|
|
openai_object = {
|
|
"id": "cmpl-99y7B2svVoRWe1xd7UFRmeGjZrFSh",
|
|
"choices": [
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 0,
|
|
"logprobs": {
|
|
"text_offset": [101],
|
|
"token_logprobs": [-0.00023488728],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.00023488728,
|
|
"1": -8.375235,
|
|
"zero": -14.101797,
|
|
"__": -14.554922,
|
|
"00": -14.98461,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 1,
|
|
"logprobs": {
|
|
"text_offset": [116],
|
|
"token_logprobs": [-0.013745008],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
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{
|
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"0": -0.013745008,
|
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"1": -4.294995,
|
|
"00": -12.287183,
|
|
"2": -12.771558,
|
|
"3": -14.013745,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 2,
|
|
"logprobs": {
|
|
"text_offset": [108],
|
|
"token_logprobs": [-3.655073e-5],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
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"0": -3.655073e-5,
|
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"1": -10.656286,
|
|
"__": -11.789099,
|
|
"false": -12.984411,
|
|
"00": -14.039099,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 3,
|
|
"logprobs": {
|
|
"text_offset": [106],
|
|
"token_logprobs": [-0.1345946],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
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{
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"0": -0.1345946,
|
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"1": -2.0720947,
|
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"2": -12.798657,
|
|
"false": -13.970532,
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"00": -14.27522,
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}
|
|
],
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},
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"text": "0",
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},
|
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{
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"finish_reason": "length",
|
|
"index": 4,
|
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"logprobs": {
|
|
"text_offset": [95],
|
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"token_logprobs": [-0.10491652],
|
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"tokens": ["0"],
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"top_logprobs": [
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{
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"0": -0.10491652,
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"1": -2.3236666,
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"2": -7.0111666,
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"3": -7.987729,
|
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"4": -9.050229,
|
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}
|
|
],
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|
},
|
|
"text": "0",
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},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 5,
|
|
"logprobs": {
|
|
"text_offset": [121],
|
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"token_logprobs": [-0.00026300468],
|
|
"tokens": ["0"],
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"top_logprobs": [
|
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{
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"0": -0.00026300468,
|
|
"1": -8.250263,
|
|
"zero": -14.976826,
|
|
" ": -15.461201,
|
|
"000": -15.773701,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
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},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 6,
|
|
"logprobs": {
|
|
"text_offset": [146],
|
|
"token_logprobs": [-5.085517e-5],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -5.085517e-5,
|
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"1": -9.937551,
|
|
"000": -13.929738,
|
|
"__": -14.968801,
|
|
"zero": -15.070363,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
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},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 7,
|
|
"logprobs": {
|
|
"text_offset": [100],
|
|
"token_logprobs": [-0.13875218],
|
|
"tokens": ["1"],
|
|
"top_logprobs": [
|
|
{
|
|
"1": -0.13875218,
|
|
"0": -2.0450022,
|
|
"2": -9.7559395,
|
|
"3": -11.1465645,
|
|
"4": -11.5528145,
|
|
}
|
|
],
|
|
},
|
|
"text": "1",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 8,
|
|
"logprobs": {
|
|
"text_offset": [143],
|
|
"token_logprobs": [-0.0005573204],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0005573204,
|
|
"1": -7.6099324,
|
|
"3": -10.070869,
|
|
"2": -11.617744,
|
|
" ": -12.859932,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 9,
|
|
"logprobs": {
|
|
"text_offset": [143],
|
|
"token_logprobs": [-0.0018747397],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0018747397,
|
|
"1": -6.29875,
|
|
"3": -11.2675,
|
|
"4": -11.634687,
|
|
"2": -11.822187,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 10,
|
|
"logprobs": {
|
|
"text_offset": [110],
|
|
"token_logprobs": [-0.003476763],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.003476763,
|
|
"1": -5.6909766,
|
|
"__": -10.526915,
|
|
"None": -10.925352,
|
|
"False": -11.88629,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 11,
|
|
"logprobs": {
|
|
"text_offset": [106],
|
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"token_logprobs": [-0.00032962486],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
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{
|
|
"0": -0.00032962486,
|
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"1": -8.03158,
|
|
"__": -13.445642,
|
|
"2": -13.828455,
|
|
"zero": -15.453455,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 12,
|
|
"logprobs": {
|
|
"text_offset": [143],
|
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"token_logprobs": [-9.984788e-5],
|
|
"tokens": ["0"],
|
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"top_logprobs": [
|
|
{
|
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"0": -9.984788e-5,
|
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"1": -9.21885,
|
|
" ": -14.836038,
|
|
"zero": -16.265724,
|
|
"00": -16.578224,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
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},
|
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{
|
|
"finish_reason": "length",
|
|
"index": 13,
|
|
"logprobs": {
|
|
"text_offset": [106],
|
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"token_logprobs": [-0.0010039895],
|
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"tokens": ["0"],
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"top_logprobs": [
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{
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"0": -0.0010039895,
|
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"1": -6.907254,
|
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"2": -13.743192,
|
|
"false": -15.227567,
|
|
"3": -15.297879,
|
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}
|
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],
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|
},
|
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"text": "0",
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},
|
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{
|
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"finish_reason": "length",
|
|
"index": 14,
|
|
"logprobs": {
|
|
"text_offset": [106],
|
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"token_logprobs": [-0.0005681643],
|
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"tokens": ["0"],
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"top_logprobs": [
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{
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"0": -0.0005681643,
|
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"1": -7.5005684,
|
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"__": -11.836506,
|
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"zero": -13.242756,
|
|
"file": -13.445881,
|
|
}
|
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],
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|
},
|
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"text": "0",
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},
|
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{
|
|
"finish_reason": "length",
|
|
"index": 15,
|
|
"logprobs": {
|
|
"text_offset": [146],
|
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"token_logprobs": [-3.9769227e-5],
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"tokens": ["0"],
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"top_logprobs": [
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{
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"0": -3.9769227e-5,
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|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 46,
|
|
"logprobs": {
|
|
"text_offset": [111],
|
|
"token_logprobs": [-0.00017088225],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.00017088225,
|
|
"1": -8.765796,
|
|
"zero": -12.695483,
|
|
"__": -12.804858,
|
|
"time": -12.882983,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 47,
|
|
"logprobs": {
|
|
"text_offset": [146],
|
|
"token_logprobs": [-0.000107238506],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.000107238506,
|
|
"1": -9.171982,
|
|
"000": -13.648544,
|
|
"__": -14.531357,
|
|
"zero": -14.586044,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 48,
|
|
"logprobs": {
|
|
"text_offset": [106],
|
|
"token_logprobs": [-0.0028172398],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0028172398,
|
|
"1": -5.877817,
|
|
"00": -12.16688,
|
|
"2": -12.487192,
|
|
"000": -14.182505,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 49,
|
|
"logprobs": {
|
|
"text_offset": [104],
|
|
"token_logprobs": [-0.00043460296],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.00043460296,
|
|
"1": -7.7816844,
|
|
"00": -13.570747,
|
|
"2": -13.60981,
|
|
"__": -13.789497,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 50,
|
|
"logprobs": {
|
|
"text_offset": [143],
|
|
"token_logprobs": [-0.0046973573],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0046973573,
|
|
"1": -5.3640723,
|
|
"null": -14.082823,
|
|
" ": -14.707823,
|
|
"2": -14.746885,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 51,
|
|
"logprobs": {
|
|
"text_offset": [100],
|
|
"token_logprobs": [-0.2487161],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.2487161,
|
|
"1": -1.5143411,
|
|
"2": -9.037779,
|
|
"3": -10.100279,
|
|
"4": -10.756529,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 52,
|
|
"logprobs": {
|
|
"text_offset": [108],
|
|
"token_logprobs": [-0.0011751055],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0011751055,
|
|
"1": -6.751175,
|
|
" ": -13.73555,
|
|
"2": -15.258987,
|
|
"3": -15.399612,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
{
|
|
"finish_reason": "length",
|
|
"index": 53,
|
|
"logprobs": {
|
|
"text_offset": [143],
|
|
"token_logprobs": [-0.0012339224],
|
|
"tokens": ["0"],
|
|
"top_logprobs": [
|
|
{
|
|
"0": -0.0012339224,
|
|
"1": -6.719984,
|
|
"6": -11.430922,
|
|
"3": -12.165297,
|
|
"2": -12.696547,
|
|
}
|
|
],
|
|
},
|
|
"text": "0",
|
|
},
|
|
],
|
|
"created": 1712163061,
|
|
"model": "ft:babbage-002:ai-r-d-zapai:v3-fields-used:84jb9rtr",
|
|
"object": "text_completion",
|
|
"system_fingerprint": None,
|
|
"usage": {"completion_tokens": 54, "prompt_tokens": 1877, "total_tokens": 1931},
|
|
}
|
|
|
|
text_completion_obj = TextCompletionResponse(**openai_object)
|
|
|
|
## WRITE UNIT TESTS FOR TEXT_COMPLETION_OBJECT
|
|
assert text_completion_obj.id == "cmpl-99y7B2svVoRWe1xd7UFRmeGjZrFSh"
|
|
assert text_completion_obj.object == "text_completion"
|
|
assert text_completion_obj.created == 1712163061
|
|
assert (
|
|
text_completion_obj.model
|
|
== "ft:babbage-002:ai-r-d-zapai:v3-fields-used:84jb9rtr"
|
|
)
|
|
assert text_completion_obj.system_fingerprint == None
|
|
assert len(text_completion_obj.choices) == len(openai_object["choices"])
|
|
|
|
# TEST FIRST CHOICE #
|
|
first_text_completion_obj = text_completion_obj.choices[0]
|
|
assert first_text_completion_obj.index == 0
|
|
assert first_text_completion_obj.logprobs.text_offset == [101]
|
|
assert first_text_completion_obj.logprobs.tokens == ["0"]
|
|
assert first_text_completion_obj.logprobs.token_logprobs == [-0.00023488728]
|
|
assert len(first_text_completion_obj.logprobs.top_logprobs) == len(
|
|
openai_object["choices"][0]["logprobs"]["top_logprobs"]
|
|
)
|
|
assert first_text_completion_obj.text == "0"
|
|
assert first_text_completion_obj.finish_reason == "length"
|
|
|
|
# TEST SECOND CHOICE #
|
|
second_text_completion_obj = text_completion_obj.choices[1]
|
|
assert second_text_completion_obj.index == 1
|
|
assert second_text_completion_obj.logprobs.text_offset == [116]
|
|
assert second_text_completion_obj.logprobs.tokens == ["0"]
|
|
assert second_text_completion_obj.logprobs.token_logprobs == [-0.013745008]
|
|
assert len(second_text_completion_obj.logprobs.top_logprobs) == len(
|
|
openai_object["choices"][0]["logprobs"]["top_logprobs"]
|
|
)
|
|
assert second_text_completion_obj.text == "0"
|
|
assert second_text_completion_obj.finish_reason == "length"
|
|
|
|
# TEST LAST CHOICE #
|
|
last_text_completion_obj = text_completion_obj.choices[-1]
|
|
assert last_text_completion_obj.index == 53
|
|
assert last_text_completion_obj.logprobs.text_offset == [143]
|
|
assert last_text_completion_obj.logprobs.tokens == ["0"]
|
|
assert last_text_completion_obj.logprobs.token_logprobs == [-0.0012339224]
|
|
assert len(last_text_completion_obj.logprobs.top_logprobs) == len(
|
|
openai_object["choices"][0]["logprobs"]["top_logprobs"]
|
|
)
|
|
assert last_text_completion_obj.text == "0"
|
|
assert last_text_completion_obj.finish_reason == "length"
|
|
|
|
assert text_completion_obj.usage.completion_tokens == 54
|
|
assert text_completion_obj.usage.prompt_tokens == 1877
|
|
assert text_completion_obj.usage.total_tokens == 1931
|
|
|
|
|
|
def test_completion_openai_prompt():
|
|
try:
|
|
print("\n text 003 test\n")
|
|
response = text_completion(
|
|
prompt=["What's the weather in SF?", "How is Manchester?"],
|
|
max_tokens=5,
|
|
**FIREWORKS_TEXT_COMPLETION,
|
|
)
|
|
print(response)
|
|
assert len(response.choices) == 2
|
|
response_str = response["choices"][0]["text"]
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_completion_openai_prompt()
|
|
|
|
|
|
def test_completion_openai_engine_and_model() -> None:
|
|
response: Final = text_completion(
|
|
model="gpt-6-luna",
|
|
engine="anything",
|
|
reasoning_effort="none",
|
|
prompt="What's the weather in SF?",
|
|
max_tokens=5,
|
|
)
|
|
assert response.model == "gpt-6-luna"
|
|
assert response.choices[0].text
|
|
|
|
|
|
# test_completion_openai_engine_and_model()
|
|
|
|
|
|
def test_completion_openai_engine() -> None:
|
|
response: Final = text_completion(
|
|
engine="gpt-6-luna",
|
|
reasoning_effort="none",
|
|
prompt="What's the weather in SF?",
|
|
max_tokens=5,
|
|
)
|
|
assert response.model == "gpt-6-luna"
|
|
assert response.choices[0].text
|
|
|
|
|
|
# test_completion_openai_engine()
|
|
|
|
|
|
def test_completion_chatgpt_prompt():
|
|
try:
|
|
print("\n gpt3.5 test\n")
|
|
response = text_completion(
|
|
model="openai/gpt-3.5-turbo", prompt="What's the weather in SF?"
|
|
)
|
|
print(response)
|
|
response_str = response["choices"][0]["text"]
|
|
print("\n", response.choices)
|
|
print("\n", response.choices[0])
|
|
# print(response.choices[0].text)
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_completion_chatgpt_prompt()
|
|
|
|
|
|
def test_completion_gpt_instruct():
|
|
try:
|
|
response = text_completion(
|
|
model="gpt-5.4-nano",
|
|
prompt="What's the weather in SF?",
|
|
custom_llm_provider="text-completion-openai",
|
|
)
|
|
print(response)
|
|
response_str = response["choices"][0]["text"]
|
|
print("\n", response.choices)
|
|
print("\n", response.choices[0])
|
|
# print(response.choices[0].text)
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_completion_chatgpt_prompt()
|
|
|
|
|
|
def test_text_completion_basic():
|
|
try:
|
|
print("\n test 003 with logprobs \n")
|
|
litellm.set_verbose = False
|
|
response = text_completion(
|
|
model="text-completion-openai/gpt-5.4-nano",
|
|
prompt="good morning",
|
|
max_tokens=10,
|
|
logprobs=10,
|
|
)
|
|
print(response)
|
|
print(response.choices)
|
|
print(response.choices[0])
|
|
# print(response.choices[0].text)
|
|
response_str = response["choices"][0]["text"]
|
|
except Exception as e:
|
|
if "502: Bad gateway" in str(e):
|
|
print("502: Bad gateway error occurred... passing")
|
|
return
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_text_completion_basic()
|
|
|
|
|
|
def test_completion_text_003_prompt_array():
|
|
try:
|
|
litellm.set_verbose = False
|
|
response = text_completion(
|
|
prompt=token_prompt, # token prompt is a 2d list
|
|
max_tokens=5,
|
|
**FIREWORKS_TEXT_COMPLETION,
|
|
)
|
|
assert len(response.choices) == len(token_prompt)
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_completion_text_003_prompt_array()
|
|
|
|
|
|
# not including this in our ci cd pipeline, since we don't want to fail tests due to an unstable replit
|
|
# def test_text_completion_with_proxy():
|
|
# try:
|
|
# litellm.set_verbose=True
|
|
# response = text_completion(
|
|
# model="facebook/opt-125m",
|
|
# prompt='Write a tagline for a traditional bavarian tavern',
|
|
# api_base="https://openai-proxy.berriai.repl.co/v1",
|
|
# custom_llm_provider="openai",
|
|
# temperature=0,
|
|
# max_tokens=10,
|
|
# )
|
|
# print("\n\n response")
|
|
|
|
# print(response)
|
|
# except Exception as e:
|
|
# pytest.fail(f"Error occurred: {e}")
|
|
# test_text_completion_with_proxy()
|
|
|
|
|
|
##### hugging face tests
|
|
@pytest.mark.skip(reason="local test")
|
|
def test_completion_hf_prompt_array():
|
|
try:
|
|
litellm.set_verbose = True
|
|
print("\n testing hf mistral\n")
|
|
response = text_completion(
|
|
model="huggingface/mistralai/Mistral-7B-Instruct-v0.3",
|
|
prompt=token_prompt, # token prompt is a 2d list,
|
|
max_tokens=0,
|
|
temperature=0.0,
|
|
# echo=True, # hugging face inference api is currently raising errors for this, looks like they have a regression on their side
|
|
)
|
|
print("\n\n response")
|
|
|
|
print(response)
|
|
print(response.choices)
|
|
assert len(response.choices) == 2
|
|
# response_str = response["choices"][0]["text"]
|
|
except litellm.RateLimitError:
|
|
print("got rate limit error from hugging face... passsing")
|
|
return
|
|
except Exception as e:
|
|
print(str(e))
|
|
if "is currently loading" in str(e):
|
|
return
|
|
if "Service Unavailable" in str(e):
|
|
return
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# test_completion_hf_prompt_array()
|
|
|
|
|
|
@pytest.mark.skip(
|
|
reason="HF Inference API is unstable, this is now the 3rd time it's stopped working"
|
|
)
|
|
def test_text_completion_stream():
|
|
try:
|
|
for _ in range(2): # check if closed client used
|
|
response = text_completion(
|
|
model="huggingface/deepseek-ai/DeepSeek-R1",
|
|
prompt="good morning",
|
|
stream=True,
|
|
max_tokens=10,
|
|
)
|
|
for chunk in response:
|
|
print(f"chunk: {chunk}")
|
|
except Exception as e:
|
|
pytest.fail(f"GOT exception for HF In streaming{e}")
|
|
|
|
|
|
# test_text_completion_stream()
|
|
|
|
# async def test_text_completion_async_stream():
|
|
# try:
|
|
# response = await atext_completion(
|
|
# model="text-completion-openai/gpt-3.5-turbo-instruct",
|
|
# prompt="good morning",
|
|
# stream=True,
|
|
# max_tokens=10,
|
|
# )
|
|
# async for chunk in response:
|
|
# print(f"chunk: {chunk}")
|
|
# except Exception as e:
|
|
# pytest.fail(f"GOT exception for HF In streaming{e}")
|
|
|
|
# asyncio.run(test_text_completion_async_stream())
|
|
|
|
|
|
def test_async_text_completion():
|
|
litellm.set_verbose = True
|
|
print("test_async_text_completion")
|
|
|
|
async def test_get_response():
|
|
try:
|
|
response = await litellm.atext_completion(
|
|
model="gpt-3.5-turbo-instruct",
|
|
prompt="good morning",
|
|
stream=False,
|
|
max_tokens=10,
|
|
)
|
|
print(f"response: {response}")
|
|
except litellm.Timeout as e:
|
|
print(e)
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
asyncio.run(test_get_response())
|
|
|
|
|
|
def test_async_text_completion_together_ai():
|
|
from openai import AsyncOpenAI
|
|
|
|
client = AsyncOpenAI(api_key="my-fake-key")
|
|
|
|
async def run_call():
|
|
with patch.object(client.completions.with_raw_response, "create", side_effect=mock_post) as mock_call:
|
|
response = await litellm.atext_completion(
|
|
model="together_ai/Qwen/Qwen2-1.5B-Instruct",
|
|
prompt="good morning",
|
|
max_tokens=10,
|
|
client=client,
|
|
)
|
|
return response, mock_call.call_args.kwargs
|
|
|
|
response, sent = asyncio.run(run_call())
|
|
assert sent["model"] == "Qwen/Qwen2-1.5B-Instruct"
|
|
assert sent["prompt"] == "good morning"
|
|
assert sent["max_tokens"] == 10
|
|
assert response.choices[0].text == ") might be faster than then answering, and the added time it takes for the"
|
|
assert response.usage.total_tokens == 18
|
|
|
|
|
|
# test_async_text_completion()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_async_text_completion_stream() -> None:
|
|
response: Final = await litellm.atext_completion(
|
|
model="gpt-6-luna",
|
|
reasoning_effort="none",
|
|
prompt="good morning",
|
|
stream=True,
|
|
max_tokens=32,
|
|
)
|
|
chunks: Final = [chunk async for chunk in response]
|
|
assert sum(chunk.choices[0].finish_reason is not None for chunk in chunks) == 1
|
|
assert any(chunk.choices[0].text for chunk in chunks)
|
|
|
|
|
|
# test_async_text_completion_stream()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_async_text_completion_chat_model_stream():
|
|
try:
|
|
response = await litellm.atext_completion(
|
|
model="gpt-3.5-turbo",
|
|
prompt="good morning",
|
|
stream=True,
|
|
max_tokens=10,
|
|
)
|
|
|
|
num_finish_reason = 0
|
|
chunks = []
|
|
async for chunk in response:
|
|
print(chunk)
|
|
chunks.append(chunk)
|
|
if chunk["choices"][0].get("finish_reason") is not None:
|
|
num_finish_reason += 1
|
|
|
|
assert (
|
|
num_finish_reason == 1
|
|
), f"expected only one finish reason. Got {num_finish_reason}"
|
|
response_obj = litellm.stream_chunk_builder(chunks=chunks)
|
|
cost = litellm.completion_cost(completion_response=response_obj)
|
|
assert cost > 0
|
|
except Exception as e:
|
|
pytest.fail(f"GOT exception for gpt-3.5 In streaming{e}")
|
|
|
|
|
|
# asyncio.run(test_async_text_completion_chat_model_stream())
|
|
|
|
|
|
def mock_post(*args, **kwargs):
|
|
mock_response = MagicMock()
|
|
mock_response.status_code = 200
|
|
mock_response.headers = {"Content-Type": "application/json"}
|
|
mock_response.parse.return_value.model_dump.return_value = {
|
|
"id": "cmpl-7a59383dd4234092b9e5d652a7ab8143",
|
|
"object": "text_completion",
|
|
"created": 1718824735,
|
|
"model": "Sao10K/L3-70B-Euryale-v2.1",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"text": ") might be faster than then answering, and the added time it takes for the",
|
|
"logprobs": None,
|
|
"finish_reason": "length",
|
|
"stop_reason": None,
|
|
}
|
|
],
|
|
"usage": {"prompt_tokens": 2, "total_tokens": 18, "completion_tokens": 16},
|
|
}
|
|
return mock_response
|
|
|
|
|
|
@pytest.mark.parametrize("provider", ["openai", "hosted_vllm"])
|
|
def test_completion_vllm(provider):
|
|
"""
|
|
Asserts a text completion call for vllm actually goes to the text completion endpoint
|
|
"""
|
|
from openai import OpenAI
|
|
|
|
client = OpenAI(api_key="my-fake-key")
|
|
|
|
with patch.object(
|
|
client.completions.with_raw_response, "create", side_effect=mock_post
|
|
) as mock_call:
|
|
response = text_completion(
|
|
model="{provider}/gemini-2.5-flash-lite".format(provider=provider),
|
|
prompt="ping",
|
|
client=client,
|
|
hello="world",
|
|
)
|
|
print("raw response", response)
|
|
|
|
assert response.usage.prompt_tokens == 2
|
|
|
|
mock_call.assert_called_once()
|
|
|
|
assert "hello" in mock_call.call_args.kwargs["extra_body"]
|
|
|
|
|
|
@pytest.mark.skip(reason="fireworks is having an active outage")
|
|
def test_completion_fireworks_ai_multiple_choices():
|
|
litellm._turn_on_debug()
|
|
response = litellm.text_completion(
|
|
model="fireworks_ai/llama-v3p1-8b-instruct",
|
|
prompt=["halo", "hi", "halo", "hi"],
|
|
)
|
|
print(response.choices)
|
|
|
|
assert len(response.choices) == 4
|
|
|
|
|
|
@pytest.mark.parametrize("stream", [True, False])
|
|
def test_text_completion_with_echo(stream):
|
|
litellm.set_verbose = True
|
|
response = litellm.text_completion(
|
|
prompt="hello",
|
|
**FIREWORKS_TEXT_COMPLETION,
|
|
max_tokens=1, # only see the first token
|
|
stop="\n", # stop at the first newline
|
|
logprobs=1, # return log prob
|
|
echo=True, # if True, return the prompt as well
|
|
stream=stream,
|
|
)
|
|
print(response)
|
|
|
|
if stream:
|
|
for chunk in response:
|
|
print(chunk)
|
|
else:
|
|
assert isinstance(response, TextCompletionResponse)
|
|
assert response.choices[0].text.startswith("hello")
|
|
assert response.choices[0].logprobs.token_logprobs
|
|
|
|
|
|
def test_text_completion_ollama():
|
|
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
|
|
|
client = HTTPHandler()
|
|
|
|
with patch.object(client, "post") as mock_call:
|
|
try:
|
|
response = litellm.text_completion(
|
|
model="ollama/llama3.1:8b",
|
|
prompt="hello",
|
|
client=client,
|
|
)
|
|
print(response)
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
mock_call.assert_called_once()
|
|
print(mock_call.call_args.kwargs)
|
|
json_data = json.loads(mock_call.call_args.kwargs["data"])
|
|
assert json_data["prompt"] == "hello"
|